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Deep Neural Networks for Chronological Age Estimation From OPG Images.
IEEE transactions on medical imaging
Jan 31, 2020
Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been dev...
Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks.
Computer methods and programs in biomedicine
Jan 25, 2020
Breast ultrasound and computer aided diagnosis (CAD) has been used to classify tumors into benignancy or malignancy. However, conventional CAD software has some problems (such as handcrafted features are hard to design; conventional CAD systems are d...
Deep pancreas segmentation with uncertain regions of shadowed sets.
Magnetic resonance imaging
Jan 24, 2020
Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have poor contrast and blurred boundaries. Second, there exist large variations in gray scale, texture, ...
Research of Multimodal Medical Image Fusion Based on Parameter-Adaptive Pulse-Coupled Neural Network and Convolutional Sparse Representation.
Computational and mathematical methods in medicine
Jan 24, 2020
Visual effects of medical image have a great impact on clinical assistant diagnosis. At present, medical image fusion has become a powerful means of clinical application. The traditional medical image fusion methods have the problem of poor fusion re...
Improvement of classification performance of Parkinson's disease using shape features for machine learning on dopamine transporter single photon emission computed tomography.
PloS one
Jan 24, 2020
OBJECTIVE: To assess the classification performance between Parkinson's disease (PD) and normal control (NC) when semi-quantitative indicators and shape features obtained on dopamine transporter (DAT) single photon emission computed tomography (SPECT...
A deep convolutional neural network architecture for interstitial lung disease pattern classification.
Medical & biological engineering & computing
Jan 22, 2020
Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues and early diagnosis of these disease patterns is crucial for the treatment. Yet it is difficult to make an accurate diagnosis due to the similarity am...
Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database.
Surgical endoscopy
Jan 17, 2020
BACKGROUND: Postoperative gastrointestinal leak and venous thromboembolism (VTE) are devastating complications of bariatric surgery. The performance of currently available predictive models for these complications remains wanting, while machine learn...
Machine learning can identify newly diagnosed patients with CLL at high risk of infection.
Nature communications
Jan 17, 2020
Infections have become the major cause of morbidity and mortality among patients with chronic lymphocytic leukemia (CLL) due to immune dysfunction and cytotoxic CLL treatment. Yet, predictive models for infection are missing. In this work, we develop...
Label Co-Occurrence Learning With Graph Convolutional Networks for Multi-Label Chest X-Ray Image Classification.
IEEE journal of biomedical and health informatics
Jan 16, 2020
Existing multi-label medical image learning tasks generally contain rich relationship information among pathologies such as label co-occurrence and interdependency, which is of great importance for assisting in clinical diagnosis and can be represent...
Understanding Opioid Use Disorder (OUD) using tree-based classifiers.
Drug and alcohol dependence
Jan 15, 2020
BACKGROUND: Opioid Use Disorder (OUD), defined as a physical or psychological reliance on opioids, is a public health epidemic. Identifying adults likely to develop OUD can help public health officials in planning effective intervention strategies. T...
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